论文标题
TDR-OBCA：在自由空间环境中自动驾驶的可靠计划者
TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment
论文作者
论文摘要
本文介绍了一种基于优化的碰撞避免轨迹生成方法，用于在自由空间环境中自动驾驶，具有增强的鲁棒性，驾驶舒适性和效率。从基于混合优化的框架开始，我们引入了两种温暖的开始方法，即时间和二元可变启动，以提高效率。我们还重新制定了问题以提高鲁棒性和效率。我们将此新算法TDR-OBCA命名。随着这些变化，与原始混合优化相比，我们相对于初始条件的失败率降低了96.67％，驾驶舒适度增加了13.53％，随着障碍量表的增加，计划者效率提高了3.33％至44.82％。我们在美国和中国的数百个模拟场景和数百小时的公共道路测试中验证了结果。我们的源代码可在https://github.com/apolloauto/apollo上找到。
This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulate the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67% failure rate decrease with respect to initial conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is available at https://github.com/ApolloAuto/apollo.
